Optimizing Data-to-Learning-to-Action

ABSTRACT

An optimizing data-to-learning-to-action method and system identifies uncertainties embodied as probability distributions that influence a sequence of decisions. The uncertainties are mapped to a simulation of a computer-based infrastructure that supports the execution of the decisions. Actions with respect to the infrastructure that are expected to reduce the uncertainties are simulated. The probability distributions are updated accordingly for each simulated action and an associated net value of information for each simulated action is generated. The action with the greatest net value of information is implemented and the simulated infrastructure is updated accordingly. The process may then be re-run based upon the updated simulated infrastructure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/230,432, filed on Aug. 7, 2016, which is a continuation ofU.S. patent application Ser. No. 14/840,567, filed on Aug. 31, 2015,which is a continuation of U.S. patent application Ser. No. 13/027,042,filed on Feb. 14, 2011, which is a continuation of U.S. patentapplication Ser. No. 11/934,750, filed on Nov. 3, 2007, which is acontinuation of U.S. patent application Ser. No. 11/329,722, filed onJan. 10, 2006.

FIELD OF INVENTION

This invention relates to decision processes and, more particularly, toprocesses and associated methods and computer-based programs in whichprobabilistic inferencing and experimental design are applied to supportdecision processes.

BACKGROUND OF THE INVENTION

Many decisions are influenced by some element of uncertainty. It isoften valuable to take actions to gather information that may, at leastin part, resolve uncertainties associated with a decision. Somecalculation methods associated with determining the value of perfect orimperfect information are known from prior art. For example, theapplication of decision tree techniques may enable the derivation ofexpected values of information associated with an information gatheringaction. These methods typically require significant manual modelingefforts.

Experimental design or “design of experiment” methods are also knownfrom prior art. These are methods of organizing experiments, or morebroadly, any type of information gathering actions, in a manner so as tomaximize the expected value of the resulting information, typically inaccordance with constraints, such as an action budgetary constraint. Forexample, factorial matrix methods are a well established approach toscientific experimental design.

These types of design of experiment methods typically require astatistician or other human expert to manually establish theexperimental design parameters, and the proper sequencing of theexperiments.

Making inferences from information attained as a result of experimentsor, more broadly, information gathering actions, is well known fromprior art. For example, in the prior art, a wide variety or statisticaltechniques are known and may be applied. These statistical techniquesgenerally require some degree of interpretation by a statistician orother expert to be applied to decisions. And, in the prior art, alimited ability to automatically conduct experimental or informationgather actions is known, but the application is invariably constrainedby the requirement of human intervention to interpret interim resultsand adjust the experimentation accordingly.

Thus, in the prior art, each of the steps of determining expected valueof information, of experimental design, of conducting experimentation,and of performing statistical or probabilistic inferencing from newinformation generated by experimentation, requires significant humanintervention. Furthermore, in prior art processes, there does not existan automatic feedback loop from the inferencing from new informationstep to the value of information and experimental design steps. Thisintroduces significant bottlenecks in addressing and resolvinguncertainties associated with decisions efficiently and effectively.This deficiency of the prior art processes and systems represents aparticularly significant economic penalty in situations in which largeamounts of relevant information is already available, or can be gatheredrapidly. For example, high throughput experimentation methods can enablerapid acquisition of new information. However, manual bottlenecks mayeffectively limit the actually attainable throughput of suchexperimental infrastructure, and, more generally, limit the mosteffective use of available historical information.

The economic penalties associated with prior art decision processes areparticularly acute in business processes such as product and/or serviceresearch and development, for which the manual interventions required indecision processes diminish both the efficiency and the effectiveness(measured in both quality and timeliness) of the decision making.

Hence, there is a need for an improved process, method, and system toresolve uncertainties associated with decisions.

SUMMARY OF INVENTION

In accordance with the embodiments described herein, a method and systemfor an adaptive decision process is disclosed. The adaptive decisionprocess, as the process is known herein, addresses the shortcomings ofthe prior art by enabling an automatic closed loop approach toinformation gathering decisions and the evaluation of the results of theinformation gathering.

Other features and embodiments will become apparent from the followingdescription, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an adaptive decision process,according to some embodiments;

FIG. 2 is a block diagram showing further details of an adaptivedecision process according to some embodiments;

FIG. 3 is a flow diagram of the adaptive decision process of FIG. 2,according to some embodiments;

FIG. 4 is a block diagram of the adaptive decision process of FIG. 2,with an alternative representation of a decision model, according tosome embodiments;

FIG. 5 is a diagram of an uncertainty resolution value framework,according to some embodiments;

FIG. 6A is a diagram of an uncertainty resolution cost framework,according to some embodiments;

FIG. 6B is a diagram of specific information sources within theuncertainty resolution cost framework of FIG. 6A, according to someembodiments;

FIG. 7 is a diagram of a net action value framework, according to someembodiments;

FIG. 8 is a diagram of an uncertainty mapping applied by the adaptivedecision process of FIG. 2, according to some embodiments;

FIG. 9 is a diagram of a value of information mapping applied by theadaptive decision process of FIG. 2, according to some embodiments;

FIG. 10 is a diagram of a design of experiment mapping applied by theadaptive decision process of FIG. 2, according to some embodiments;

FIG. 11 is a diagram of a statistical inferencing mappings applied bythe adaptive decision process of FIG. 2, according to some embodiments;

FIG. 12 is a diagram illustrating additional aspects of the statisticalinferencing function, according to some embodiments;

FIG. 13 is a diagram illustrating the updating of uncertainty mappingsand values of information, according to some embodiments;

FIG. 14 is a diagram of a support vector machine design of experimentimplementation, according to some embodiments;

FIG. 15 is a diagram of a support vector machine design of experimentimplementation step based on FIG. 14, according to some embodiments;

FIG. 16 is a flow diagram of an experimental infrastructure decisionprocess, according to some embodiments;

FIG. 17 is a diagram of an example outcome of the experimentalinfrastructure decision process of FIG. 16, according to someembodiments;

FIG. 18 is a diagram of a computer-based process implementation of theadaptive decision process of FIG. 2, according to some embodiments;

FIG. 19 is a diagram of an adaptive computer-based processimplementation of the adaptive decision process of FIG. 2, according tosome embodiments; and

FIG. 20 is a diagram of alternative computer-based system configurationswith which the adaptive decision process of FIG. 2 may operate,according some embodiments.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to providean understanding of the present invention. However, it will beunderstood by those skilled in the art that the present invention may bepracticed without these details and that numerous variations ormodifications from the described embodiments may be possible.

In accordance with the embodiments described herein, a method for anadaptive decision process, and a system enabling the adaptive decisionprocess, are disclosed. In some embodiments, the adaptive decisionprocess utilizes the methods and systems of generative investmentprocesses as disclosed in PCT Patent Application No. PCT/US2005/001348,entitled “Generative Investment Process,” filed on Jan. 18, 2005, andmay apply the methods and systems disclosed in PCT Patent ApplicationNo. PCT/US2005/011951, entitled “Adaptive Recombinant Processes,” filedon Apr. 8, 2005, which are both hereby incorporated by reference as ifset forth in their entirety.

FIG. 1 summarizes an exemplary architecture 300 of one embodiment of theadaptive decision process. A decision is established and modeled 310.The decision may be represented in a decision tree form 311, althoughother models for representing the decision may be applied. The decisiontree model 311 may be derived from other decision modeling techniques,such as influence and/or relevance models or diagrams. The decisionmodel 311 is comprised of a current decision 312, one or more potentialactions 314 that must be decided upon, and one or more expected futurestates 316 that are the expected consequences of performing the one ormore actions 314. The future states are influenced by one or moreuncertain variables (UV) 318. The uncertain variables 318 may be modeledmathematically as discrete or continuous probability functions, and theassociated future states 316 may be discrete, or they may be representedmathematically as a continuous function or functions. Continuousfunctions may be discretized as required to make the decision model 311more manageable.

Note that an uncertain variable 319 may influence more than one expectedfuture states 316.

Second order future decisions 313 may be identified, conditional on thefirst order expected future states 316, and these second order futuredecisions 313 may be associated with second order future actions 315that may lead to a next order or level of future states 317. Additionallevels of decisions, associated actions, future states, and associateduncertain variables may be “chained together” without limit.

An evaluation function 320 may be applied 321 to support thedetermination of one or more appropriate actions 314. The evaluations ofthe one or more potential actions 314 that comprise a current decision312 by the evaluation function 320 may be based on decision criteriathat include expected financial benefits, net of expected costs. Thesefinancial metrics may include discounting cash flows, thereby yielding anet present value. Alternatively, option-based valuations may be used.Other financial metrics such as internal rate of return or payback timemay be used, although these metrics may require additional adjustmentsto achieve proper results. The net benefits may be adjusted byexpectations or probabilities of success, to yield an expected netbenefit associated with a decision 312 and its corresponding potentialactions 316. (“Investment Science,” Luenberger, 1998, provides a surveyof the current art with regard to investment modeling.)

The evaluation function 320 may apply adjustments to the calculatedvalue of an action based on factors such as risk (i.e., variance inexpected outcomes), including application of utility functions thatincorporate risk. In some embodiments, the evaluation function applies ametric to each “leaf” node of the decision tree framework 311, and thencalculates backward to the current decision 312 to determine theexpected values of each possible action path within the decision treemodel 311. The action 314 with the largest expected value may then bechosen to be executed. The examples of financial and non-financialcriteria applied by the evaluation function 320 described herein aremerely illustrative and not exhaustive. The evaluation function 320 mayapply one or more of the financial and non-financial criteria.

The decision model 310 can be considered to address and/or represent thedirect, proximal, or “primary” decision 312. However, there also existsan indirect or “meta-decision”: the decision as to whether to attainadditional information that would be expected to resolve, to at leastsome degree, uncertainties corresponding to uncertain variables 318 thatare associated with the primary decision 312, prior to making theprimary decision 312. The experimental design and inferencing function340 addresses this meta-decision 331, and the associated one or morepotential actions 333 expected to result in attainment of additionalinformation that reduce uncertainties associated with the one or moreuncertain variables 318. The actions 333 may be comprised of bothactions 314 associated with the primary decision 312, which may beexpected to produce uncertainty resolution information as a “by-product”of execution of the action 314, as well as actions 336 that are totallyor primarily for the purpose of generating information that is expectedto assist in resolving uncertainties associated with uncertain variables318. The expected net values 335 of each potential action of the set ofall potential actions 333 may be determined by the experimental designand inferencing function 340. For actions that may be consideredindependent, the action from the set of all potential actions 333 withthe highest positive expected net value may be selected for execution.Depending on timing factors and correlations among actions 333, morethan one action may be selected for execution. If none of the actions333, individually of collectively, has an expected net value greaterthan zero, then no explicit actions regarding attainment of additionalinformation should be conducted.

The expected net values 335 of one or more actions 333 may include theexpected value of the information that will result from action 333 basedon the expected degree of resolution of uncertainty associated with theone or more uncertain variables 318, as well as the cost of conductingthe action 333. In some embodiments, a value adjustment associated withthe expected amount of time to attainment of the information resultingfrom the action 333 may be applied.

As indicated above, in some embodiments, the actions 333 associated withattaining additional information may include actions 314 associated withthe primary decision 312. The expected net value of information 335associated with these actions 314 may thus be calculated directly withinthe experimental design and inferencing function 340. In otherembodiments, this value 334 is determined directly by the evaluationfunction 320 as it is applied to the decision model 310.

The experimental design and inferencing function 340 interacts with 322the decision model 310. The interactions 322 may be directly within asingle computer-based software model, or across multiple computer-basedsoftware models or model components, potentially residing on differentcomputer-based systems.

The experimental design and inferencing function 340 may interact 323with an information gathering means 350 and/or an interact 324 with aninformation base 360. The information gathering means 350 may includeany automatic or non-automatic methods or systems for executing actions333 to attain additional information. These methods and/or systems mayinclude, but are not limited to, computer-based search functions,computer-based data analysis functions, data mining functions,information retrieval systems, computer-based intelligent agents, humanexpert networks, surveys, and process control systems to operateexperimental or information gathering infrastructure, includinginstrumentation, sensors, robotics, and other apparatus than may be usedto gather information relevant to the decision model 310. Theinformation gathering means 350 and/or its constituent parts may also betermed “information gathering infrastructure”, “experimentalinfrastructure”, or just “infrastructure” herein. The information base360 may contain information that has been attained through applicationof the information gathering means 350, or from other means or sources.The information may be in digital form, and may be stored in datastructures that are organized as hierarchies, networks, or relationaltable structures. Although information gathering means 350 andinformation base 360 are depicted as external to experimental design andinferencing function 340 in FIG. 1, either may be internal toexperimental design and inferencing function 340 in some embodiments. Inother embodiments, information gathering means 350 and information base360 may be external to the adaptive decision process 300, rather thaninternal as depicted in FIG. 1. In any of these organizing topologies,information gathering means 350 may be able to directly transferinformation 325 to the information base 360, and the transfer ofinformation 325 may be through any type of communications link ornetwork. Likewise, the transfer of information 323 between theexperimental design and inferencing 340 and the information gatheringmeans 350, and the transfer of information 324 between the experimentaldesign and inferencing 340 and the information base 360, may be within asingle computer, or across a computer network, such as the Internet.

FIG. 2 depicts more details of the functions associated with theexperimental design and inferencing function 340, in accordance with apreferred embodiment. The experimental design and inferencing function340 may include one or more of: an uncertainty mapping function 341, avalue of information function 342, a design of experiment function 344,and a statistical inferencing or learning function 346.

The uncertainty mapping function 341 includes a mapping of uncertaintiescorresponding to uncertain variables 318 of the decision model 310 withactions 333 and other decision-related variables and information. Thequantification of the uncertainties may be based on subjectiveassessments, or may be derived from statistical or probabilisticmodeling techniques applied to sets of data.

The value of information function 342 enables the generation of absoluteand/or relative values of perfect or imperfect information associatedwith resolving uncertainties associated with uncertain variables 318 ofthe decision model 310, as defined by the uncertainty mappings 341A (seeFIG. 8), and based, at least in part, on input 326 from the uncertaintymappings function 341.

Based, at least in part, on value of information inputs 326 a from thevalue of information function 342 and optionally on uncertainty mappinginputs 327 of the uncertainty mappings function 341, a design ofexperiment or experiments, (also termed an “experimental design”herein), or more broadly, an experimental plan, for achieving additionalinformation may be generated by the design of experiment function 344.It should be understood that the term “experiment,” as used herein, doesnot necessarily only imply scientific information gathering. Rather,“experiment”, as used herein, should be understood to encompass anyaction to attain information intended to resolve uncertainties,regardless of the domain or field of application.

In addition to the value of the information itself, the expected cost ofconducting experiments or gathering information may be incorporated bythe design of experiment function 344 in determining an effectiveinformation gathering plan. Dependencies or correlations among theuncertainties associated with the uncertain variables 318 of thedecision model 310 are incorporated to generate a suggested possiblesequencing of experiments or information gathering actions 336. Thegeneration of the sequence of experiments 336 may be enabled by anoptimization algorithm. The optimization algorithm may utilize aBayesian network model and/or a dynamic programming model, a statisticallearning model, or one or more other models or algorithms that enableoptimization of stochastic processes.

Further, in addition to the adaptive decision process 300 applying thevalue of information function 342 to determining the value associatedwith a specific decision associated with a decision model 310, the valueof information function 342 may be applied to longer-term decisionsregarding the means of information gathering or experimentalinfrastructure 350 required on an ongoing basis. If attaining additionalinformation decisions 331 are considered “meta-decisions” associatedwith direct decisions 312, then decisions on the development andmaintenance of the infrastructure 350 to perform the meta-decisions 331may be considered “meta-meta-decisions.” The value of information formultiple expected future direct decisions 312 and correspondinginformation gathering decisions 331 may be aggregated to determine thevalue of various test infrastructure alternatives. Subtracting theexpected fixed costs of the infrastructure, as well as the expectedvariable costs (i.e., per unit costs), from the expected value ofinformation gains from the expected use of the infrastructure 350provides evaluation criteria that can be applied to support decisions oninfrastructure alternatives. This information gathering infrastructure350 may include, for example, high throughput experimentationinfrastructure for materials science or life sciences applications,digitized knowledge bases of content or data, and stationary or mobilesensing instrumentation. FIG. 16 and the accompanying descriptiondescribe the process for deciding on changes or enhancements to theexperimental infrastructure 350 in more detail. The results ofexperiments conducted by the experimental infrastructure 350 may bedelivered to 323, and then evaluated or analyzed, by the statisticalinferencing function 346. The degree of resolution of uncertainties maybe delivered to 328 a the uncertainty mapping function 341, be assignedto the corresponding elements of the uncertainty mapping 341 a, and maybe fed back 329 to the value of information function 342 and/or fed back328 to the design of experiment function 344. In FIG. 2, the functions341, 342, 344, and 346 are shown interrelating with one another; thesefunctions are described generally as experimental design and inferencing340, as performed by the adaptive decision process 300.

In accordance with some embodiments, FIG. 3 depicts the overall processflow 700 performed by the adaptive decision process 300. In the firststep of the process, a decision model and associated outcome evaluationcriteria are established 702, corresponding to decision model 310 andevaluation function 320 of FIG. 2.

Corresponding to, and/or applying, the value of information function 342of the experimental design and inferencing function 340 of FIG. 2, theexpected net value of actions specifically associated with providinginformation to resolve uncertainties of uncertain variables 318 of thedecision model 310 is then determined 704. A determination 706 ofwhether the expected net value 338 of at least one of these actions 336is positive is then conducted. If none of the expected net values 338 ofthe information gathering-specific actions 336, individually orcollectively, is positive, then no information gathering-specificactions 336 are conducted. Or, if expected net values 338 of theinformation gathering-specific actions 336 that have a higher value thanthe expected net values 334 of the potential primary decision actions314 do not exist, then only actions 314 associated with the primarydecision 312 are considered further. (To the extent actions 314associated with the primary decision 312 are expected to generate atleast some information relevant to reducing uncertainty with regard touncertain variables 318, then some or all of the methods and systems ofthe experimental design and inferencing function 340 may still beapplied.)

If at least one of the expected net values 338 of the informationgathering actions 336, individually or collectively, is positive, thenthe actions to conduct are determined 708. Step 708 corresponds to,and/or may apply, the design of experiment function 344 of theexperimental design and inferencing function 340 of FIG. 2. Informationgathering actions 336 that are positive in value are individuallyprioritized. Sets of actions 336 may also be evaluated and prioritized.Based on the individual or collective prioritizations, one or moreactions 336 may be selected 708 to be conducted. If more than one actionis determined 708 to be conducted, a suggested sequencing of the actionsthat maximizes the net value of the set of actions may be generated.

The actions 336 are then conducted 710. The actions may be conducted 710through application of the information gathering means 350. Results ofthe actions 336 are then evaluated 712. The evaluation may occur as theaction(s) 336 is conducted, through interpretation of preliminaryresults, or may be performed after the action 336 is completed. Theevaluation of the information resulting from the actions may beconducted by applying statistical algorithms or models of thestatistical inferencing function 346 of the experimental design andinferencing function 340.

Based on the evaluation of the results of the action(s) 336, thecorresponding uncertain variables 318 of the decision model 310 areupdated 714 through application of the uncertainty mappings function 341of the experimental design and inferencing function 340. The updating ofthe uncertain variables may be performed through the updating of theprobability density or distribution functions associated with theuncertain variables 318. This updating step 714 then enables 716 a newset of expected net value of actions to be determined 704 based on theupdated uncertain variables. Thus, a feedback loop 716 is established,and the process continues until there are no longer informationattaining actions 336, individually or collectively, that have positivenet value.

In some embodiments, some or all of steps of the adaptive decisionprocess as shown in FIG. 3 are automated through computer-basedapplications. Steps 702, 704, 706, 708, 712, 714, and 716 may all beembodied in computer-based software programs, and each step may operateon a fully automatic basis, or on a semi-automatic basis. (The term“automatic”, as used herein, is defined to mean without direct humaninterventions, that is, under computer-based software control. The terms“semi-automatic” or “semi-automatically,” as used herein, are defined tomean that the described activity is conducted through a combination ofone or more automatic computer-based operations and one or more directhuman interventions.) The process step of conducting the actions 710 maybe fully automated when the actions address information that is embodiedin computer applications, such as in automatically searching and/oraccessing and/or analyzing digitally encoded information. Analyzingdigitally encoded information may include applying data mining systems.Conducting the actions 710 may also be fully automated when the actionsconstitute applying automated testing infrastructure, such as, forexample, high throughput experimentation methods or other types ofautomated or semi-automated instrumentation or apparatus. Suchapproaches may include the application of process and systems thatinclude combinations of software, hardware and/or apparatus components,such as robotic-based experimentation methods, sensors, apparatus underdirection of process control systems, and/or other types of automatedinstrumentation.

FIG. 4 illustrates that the decision model 310 may be represented inother than decision tree-type formats in some embodiments. Thesealternative representations may include elements that map to a decisiontree format, however. For example, the decision 312 c of decision model310 in FIG. 4 relates to finding the best mix and quantities ofcomponents that constitute a product or service that meet criteriaassociated with one or more properties. The term “component” as usedherein, may include tangible or intangible elements, including, but notlimited to, molecules, formulations, materials, technologies, services,skills, relationships, brands, mindshare, methods, processes, financialcapital and assets, intellectual capital, intellectual property,physical assets, compositions of matter, life forms, physical locations,and individual or collections of people.

Thus, decision model 310 in FIG. 4 is comprised of a table ormatrix-oriented structure 311 c. The one or more components, andassociated quantities, as represented by “Component A” 314 ca, have aneffect, represented by component effect instance “ECA4” 318 ca 4, on oneor more properties, represented by property instance “Property 4” 316 p4. In this case, the quantity of a component instance 314 ca (where thequantity may be zero), can be considered an action 314 as depicted inthe decision tree format 311. The property instance 316 p 4 may beconsidered an expected future state 316 as depicted in the decision treeformat 311. And the effect 318 ca 4 of the component instance 314 ca onthe property instance 316 p 4 may be considered an uncertain variable318 as depicted in the decision tree format 311.

The tabular or matrix decision model representation 311 c of theadaptive decision process may be particularly applicable to formulationor composition-based product development in areas, such as, but notlimited to, pharmaceuticals, chemicals, personal care products, andfoodstuffs and beverages. The decision model representation 311 c ofFIG. 4 may also apply advantageously to other materials-based productsapplications such as electronics, building materials, and the lifesciences in general. The decision model representation 311 c may alsoeffectively apply in developing digitally based products such assoftware and media-based products such as texts, videos, songs, and anyother digitally represented product that may be “tested” by manual orautomated means.

Value of Actions

In accordance with some embodiments of the value of information function342 of the experimental design and inferencing function 340, theexpected net value of an action can be defined as a function of theexpected direct value (non-informational value) of the action, the valueof information generated by the action, and the expected cost of takingthe action. The value relationship can be written in equation form asfollows:

Expected Value of Action X=Expected Direct Value of Action X+ExpectedInformational Value of Action X−Expected Cost of Action X

Actions 336 whose value is wholly or primarily expected to derive frominformational value traditionally are generally referred to by specific,special nomenclature, such as “experiments”, “information gathering”,and “business intelligence.” Examples of specific actions 336 primarilyaimed at resolving uncertainty include financial and other businessmodeling, business and competitor intelligence, customer and marketintelligence and feedback, computer-based system user feedback, fundingsource analysis, feasibility studies, intellectual property analysis andevaluations, product (where product may include or be a service orsolution) development testing and experimentation, prototyping andsimulations. Product testing may include in vitro and in vivo testing,in silico modeling approaches, including molecular modeling,combinatorial chemistry, classic bench scale testing, high throughputexperimentation or screening methods, clinical trials, and field tests.(“Experimentation Matters,” Thomke, 2003, provides a relevant overviewof current art regarding experimentation.) Other types of actions 314may have other, primarily non-informational generated aims, but may beexpected to provide relevant information as a by-product. Deciding 312to defer an action to a definite or indefinite future time may alsologically be defined as an explicit action 314, thereby promotingcompleteness and consistency in considering action alternatives inadaptive decision process 300.

According to some embodiments, FIG. 5 depicts a framework 66 associatedwith generating the expected value of potential actions 333 that can beexpected to reduce uncertainties associated with a decision 312. Theaction value framework 66 may be applied by the value of informationfunction 342 of the adaptive decision process 300. The framework 66includes three dimensions. The first dimension 66 a is the degree towhich an action is expected to resolve uncertainty associated with anuncertain variable 318. This value can range from no expected resolutionof the associated uncertainty, to an expectation of complete resolutionof the associated uncertainty given the action is taken. The seconddimension 66 b is the expected time required from initiation of theaction to the time of the availability of the information or theinterpretation of the information generated by the action. The thirddimension 66 c is the value of the information associated with theaction, given specific values associated with the other two dimensions.

Mappings 68 a and 68 b within the framework 66 are examples ofrepresentations of the magnitude of the value of information associatedwith resolving uncertainties 66 c of an uncertain variable 318 as afunction of the expected degree of resolution of the uncertainties 66 a,and the expected time to resolve the uncertainties 66 b. The mappingthus reflects the value of the expected results of potential actions 333resulting in new information having a bearing on the uncertain variable.Each mapping may be described as a value function associated with anuncertain variable (UV) 318, which may be more generally described asValue(UVn), for any uncertain variable UVn, where there exist nuncertain variables 318 in decision model 310, and n is an integer.

For example, mapping 68 a represents the information value across therange of degrees of resolution of uncertainty 66 a, and across the rangeof the durations required to achieve the resolution of uncertainty 66 b,associated with the uncertain variable UVz. Mapping 68 b represents theinformation value across the range of degrees of resolution ofuncertainty 66 a, and the across the ranges of the duration required toachieve the resolution of uncertainty 66 b, associated with anotheruncertain variable UVy. Although the example mappings 68 a and 68 b areshown as linear, it should be understood that in general the value ofinformation mappings may be non-linear or discontinuous.

The value of information (perfect or imperfect) mapping may be derivedby the value of information function 342 through decision tree modelingtechniques associated with decision model 310, and the application ofthe evaluation function 320. Alternatively, the value of information maybe calculated through the application of other mathematical modelingtechniques, including Bayesian approaches, Monte Carlo simulations, ordynamic programming modeling incorporating stochastic variables. Thevalue of information may also be affected by other variables associatedwith the decision makers, such as risk profiles and other utilityfunctions. (The Stanford University manuscript, “The Foundations ofDecision Analysis,” Ronald A. Howard, 1998, provides a relevant reviewof value of information calculation methods.)

Decisions to defer actions for a certain amount of time may beconsidered explicit actions 333. The time dimension 66 b in theframework 66 takes into account the timing aspect of the value ofinformation function. Further, the degree of resolving uncertaintydimension 66 a may not necessarily have a value of zero when deferringan action—additional relevant information may be expected to revealitself even when no active action is conducted. In other words, such apositive expected value of information represents the value of waitingassociated with a specific action.

FIG. 6A depicts a framework 70 for evaluating the cost of actions 336 toresolve uncertainty that may be applied by the value of informationfunction 342 of the adaptive decision process 300, in some embodiments.The uncertainty resolution cost framework 70 features three dimensions.The first dimension 70 a is the degree to which the action is expectedto resolve uncertainty. The second dimension 70 b is the expected timeit will take to perform the action and interpret the results of theaction to resolve the uncertainty. The third dimension 70 c is theexpected cost of conducting the associated action to resolve theuncertainty, as a function of the other two dimensions of the framework70. Ignoring the impact of the absolute or relative value of theresulting information, it may be desirable to take actions, to theextent they exist, that are expected to be low-cost, timely, and able tosignificantly resolve uncertainties, as exemplified by the region 72within the framework 70. The general prioritization of actions on thisbasis is illustrated by the “decreasing priority” line 71 within theframework 70.

FIG. 6B illustrates specific types or classes of information gatheringmeans 350 within the context of framework 70. The information gatheringmeans 350 types depicted in FIG. 6B may be particularly applicable tocomposition of matter-based product development decisions in fields suchas pharmaceuticals and chemicals. Other fields may include differenttypes of information gathering means 350.

The example types of information gathering means 350 within framework 70of FIG. 6B includes in silico modeling 72 a, high throughput testing 72b, bench scale testing 72 c, pilot plant testing 72 d, and plant testing72 e. The arrangement of information gathering means 350 examples isconsistent with each example's positioning versus the three dimensions70 a, 70 b, 70 c of the uncertainty resolution cost framework 70. Forexample, in silico modeling 72 a, which may include any type of computersoftware-based modeling or simulation, is typically less expensive andfaster to conduct than actual physical testing, also typically providesless ability to resolve uncertainty 70 a than physical testing. At theother extreme, actual testing of products that have been produced bycommercial plants 72 e typically provides the greatest resolution ofuncertainty 70 a, but is also typically more expensive and slower toconduct than in silico modeling 72 a, or other smaller scale physicaltesting means such as high throughput testing 72 b, bench-scale testing72 c, or pilot plant-scale testing 72 d.

FIG. 7 depicts a framework 74 for evaluating the value of actions 333versus the cost of actions 333 to resolve uncertainty that may beapplied by the value of information function 342 of the adaptivedecision process 300, in some embodiments. The framework 74 comprisesthree dimensions. The first dimension 74 a is the degree to which theaction is expected to resolve uncertainty. The second dimension 74 b isthe expected time it will take to perform the action and interpret theresults of the action to resolve the uncertainty. The third dimension 74c is the expected value and cost of taking the associated action toresolve the uncertainty. A value map 76 of all values of informationassociated with a potential set of actions relating to a particularuncertain variable, UVx is shown. One particular action, “Action Z”,selected from the set of all possible of these actions has an expectedvalue as shown by point 78 b of the value mapping 76. The associatedcost of Action Z is shown as 78 a. The net value of the action istherefore the difference 78 c. This difference may be generallydescribed by the function NetValue(Action Z, UVx). Although not shown inFIG. 7, an action may provide valuable information associated with morethan one uncertain variable. In such cases the total net value of theaction in the summation of the net values of the action across alluncertain variables.

The net value of all possible actions associated with the uncertainvariables 318 of the decision model 310 may be calculated, such thatthose actions with a positive net value may be considered for execution.If two or more actions both have positive net value but are mutuallyexclusive, then the one with the greater net value may be selected forexecution, as one possibility.

Alternatively, a budget limit or constraint may be imposed. In thesecases, the net value of all possible actions may be ranked, and acumulative cost may be generated by the value of information function342, starting with the highest positive net value action and ending withthe lowest positive net value action. All actions may be executed thatare associated with cumulative cost less than or equal to the budgetconstraint.

The net values of information associated with multiple actions may notbe completely independent, and therefore simple summations of the netvalues of the actions may not be appropriate. Rather, sets of actionsmay be considered, and the set of actions with the highest net value maybe selected, conditional on budgetary or other cost limitations, andconditional on the collective duration of the set of actions. Thecollective duration of the set of actions is a function of the degree towhich actions may be conducted in parallel as opposed to being conductedin sequence.

Design of experiment approaches may be employed by the design ofexperiment function 344, to contribute toward making the most effectivechoices on actions 333. These approaches may include, but are notlimited to, applying factorial experimental designs, or other design ofexperiment decision techniques such as D-optimal designs, or applyingstatistical learning models, such as nearest neighbor models, supportvector machine models, or neural network models.

In accordance with the net action value framework 74, the design ofexperiment function 344 may determine actions to perform within thecontext of information gathering means 350. The one or more actionsdetermined may be within a certain type of information gathering means,or may be across multiple information gathering means types. Forexample, with regard to the information gathering means types depictedin FIG. 6B, the design of experiment function 344 may determineexperimental actions to conduct within one type or class of informationgathering means 350, say, high throughput testing 72 b. In otherembodiments, the design of experiment function 344 may determineexperimental actions to conduct across more than one type or class ofinformation gathering means 350, such as across high throughput testing72 b, bench-scale testing 72 c, and pilot plant testing 72 e.

Experimental Design and Inferencing Functions

Recall from FIG. 2 that the adaptive decision process 300 may include anexperimental design and inferencing process 340. The experimental designand inferencing process 340 addresses uncertainties that may exist withregard to uncertain variables 318 in the decision model 310. In FIGS.8-13, the functions of the experimental design and inferencing process340 are described in more detail.

In FIG. 8, an uncertainty mapping 341A is depicted, according to someembodiments. The uncertainty mapping 341A represents correspondencesbetween expected future states 316 and associated uncertain variables318 of the decision model 310. In the mapping 341A, each row is apair-wise association between a specific expected future state and aspecific uncertain variable. For example, in row 402, “expected futurestate 1” has a single associated uncertain variable, “uncertain variable1” 422. However, an expected future state 316 may have more than oneassociated uncertain variables. For example, as shown in rows 404, 406,and 408, “expected future state 2” has three associated uncertainvariables, “uncertain variable 1”, “uncertain variable 2”, and“uncertain variable 3”.

An uncertain variable may be not unique to a specific expected futurestate 316. For example, expected future state 2 and expected futurestate 3 both have a common corresponding uncertain variable 2 (rows 406and 410). Or, the uncertain variable may be unique to a particularexpected future state 316. For example, uncertain variable 4 is uniqueto expected future state 3 in the uncertainty mapping 341A.

One or more uncertainty mappings 341A may be included within theuncertainty mapping function 341 of the experimental design andinferencing function 340. The one or more uncertainty mappings 341A maybe generated or modified by the uncertainty mapping function 341 as newinferences are delivered from 328 a the statistical inferencing function346. The one or more uncertainty mappings 341A may be stored within acomputer-based system, preferably through a database management system,such as a relational data base system.

In FIG. 9, a mapping of probabilistic models, data, and values ofinformation to uncertain variables is depicted, according to someembodiments, described herein for convenience as a value of informationmapping 342A. Recall that the value of information function 342 enablesa determination of absolute and relative values of perfect or imperfectinformation associated with uncertain variables 318 within the decisionmodel 310, as defined by the uncertainty mappings 341 (see FIG. 2). Thevalue of information mapping 342A depicted in FIG. 9 representscorrespondences between uncertain variables 318, probabilistic models424, data or information sets 426, and uncertain variable (UV) specificvalues of information 428. The probabilistic models 424 associated withuncertain variables 422 may include one or more discrete or continuousprobability density or distribution functions. Bayesian models may beapplied, where appropriate. The data sets 426 associated with uncertainvariables 422 represent a corresponding collection of relevant raw data,processed data or information, and/or insights or knowledge derived fromthe data and information. In Bayesian terms, data sets 426 may beinterpreted as the prior state of information. Some or all of data sets426 may be included in information base 360.

The uncertain variable-specific values of information 428 associatedwith uncertain variables 318 represent the expected gross value ofattaining varying degrees of additional information incremental to theexisting body of information or data sets 426 associated with theuncertain variables 318. The uncertain variable-specific values ofinformation 428 may be generated by applying the uncertainty resolutionvalue framework 66 of FIG. 5.

The gross (meaning not net of costs to resolve the uncertainty)uncertain variable-specific value of information 428 is determined fromthe expected financial or non-financial values associated with expectedfuture states or outcomes 316, combined with levels of certaintyassociated with the outcomes of the corresponding uncertain variable422. The evaluation function 320 may be applied in assisting in thedetermination of the value of information. The uncertainvariable-specific values of information 428 may therefore includemultiple expected values, each expected value corresponding to adifferent set of potential incremental data or information 426, suchthat each data set 426 have a corresponding expected effect on the levelof uncertainty associated with the value. The uncertainvariable-specific value of information 428 may be represented by amathematical function that represents the gross value of information asa function of the degree of certainty associated the uncertain variable422. One particular value that may be calculated is the (gross) value ofperfect information, which is defined as the value of attaining perfectforesight on the outcome of the corresponding uncertain variable.Attaining less than perfect foresight, or imperfect information, mayalso provide value, but the gross value of imperfect information can beno greater than the bound that is set by the gross value of perfectinformation.

The gross (i.e., prior to subtracting the cost of attaining theinformation) uncertain variable-specific value of information 428 forone or more degrees of certainty associated with an uncertain variable422 may be calculated from the application of decision tree models,decision lattices, simulations, dynamic programming, or other modelingtechniques. Design of experiment modeling, including, but not limitedto, factorial matrices, D-optimal and statistical learning models, maybe applied to derive value of information 428 either directly or as aby-product of experimental design determinations. In addition,statistical learning models, such as support vector machine modeling maybe directly applied to derive, or assist in the derivation, of value ofinformation.

One or more value of information mappings 342A may be included withinthe value of information function 342 of the experimental design andinferencing function 340. The one or more value of information mappings342A may be generated or modified by the value of information function342. The one or more value of information mappings 342A may be storedwithin a computer-based system, preferably through a database managementsystem, such as a relational database system.

In FIG. 10, a design of experiment mapping 344A is depicted, accordingto some embodiments. One or more design of experiment mappings 344A maybe included in design of experiment function 344 of the experimentaldesign and inferencing function 340. Recall that, in addition todetermining the action-specific value of information, the expected costof conducting experiments or gathering information may also beincorporated by the design of experimental design function 344 indetermining an effective information gathering plan. The design ofexperiment mapping 344A includes an action/value mapping 450 and anexpected net value of experiment or action mapping 460. It should beunderstood that “experiment” represents just one type of the moregeneral class of “information gathering actions” 336 or just “actions”314.

The action/value mapping 450 represents correspondences betweenuncertain variables 318 and information gathering actions 333, and theinformation gathering actions 333 and expected new information generatedby each potential action 452, and the corresponding uncertainvariable-specific gross value of information associated with eachpotential action 454. As shown in the action/value mapping 450, eachuncertain variable 318 may have one or more actions 333 associated withit. An action 333 may contribute to uncertainty resolution of one ormore uncertain variables 318. The total expected gross value of anaction 314, 336 may be calculated by summing its contributions to theresolving uncertainty across all the uncertain variables 318 it isexpected to effect. So, for example, in FIG. 10, the expected grossvalue of information of “action 3” would be the sum of its contributionsto resolving uncertainties associated with both “uncertain variable 2”and “uncertain variable 5”.

The expected net value of experiment or action mapping 460 representscorrespondences between actions 333 the costs of the actions 461, andthe net values of the actions 335. The net value of the action 335 iscalculated by subtracting the cost of the action 461 from the expectedgross value of information associated with the action 333. The expectedgross value of information of the action is calculated by summing thevalue contributions of the action across uncertain variables in theaction/value mapping 450 of FIG. 10.

The design of experiment function 344 may include algorithms to assess acollection of actions 333 wherein the individual actions 333 do notnecessarily produce independent results 452, to determine what subset ofthe collection of actions to conduct in a first time period. In otherwords, where the collection of actions may result in an “overlap”associated with incremental information 452 generated by individualactions 333, in the sense of the associated incremental information 452generated by the actions 333 having some degree of correlation; thedesign of experiment function 344 may assess collections of actionsrather than just individual actions. In such cases, the design ofexperiment function 344 will assess the net value of informationassociated not only with the individual actions within the collection ofactions, but also with the net value of information associated withsubsets of the collection of actions. The design of experiment function344 may include processes or algorithms based on design of experimentmodeling such as factorial matrices or D-optimal models, or statisticallearning models, such as support vector machine models, or Bayesianmodels.

In FIG. 11, a statistical inferencing mapping 346A is depicted, whichmay be included in the statistical inferencing function 346 of theexperimental design and inferencing function 340, according to someembodiments. The statistical inferencing mapping 346A includes anexperimental or information gathering action results mapping 480 and aprobabilistic updating of uncertain variables mapping 490. Theexperimental or information gathering action results mapping 480represents a mapping of executed actions 314A,336A (corresponding topotential actions 314,336), experimental data attained by the executedactions 452A, and the uncertain variables 318 to which the experimentaldata attained by the executed actions 314A,336A corresponds. A specificinstance of the attained experimental data 452A may map to more than oneof the uncertain variables 318.

The probabilistic updating of uncertain variables mapping 490 representsthe mapping of uncertain variables 318 to updated probabilistic models424A and updated data sets 426A (the instances of the updatedprobabilistic models 424A and data sets 426A are designated as updatedby appending the “+” symbol to the corresponding items in theprobabilistic updating of uncertain variables map 490). The updated datasets 426A represent the body of data, information or knowledgeassociated with an uncertain variable 318 after the experiment orinformation gathering action 333 has been conducted and the resultsassimilated.

The updated data sets 426A therefore represent the additionalinformation 452A from the experimental or data gathering actions314A,336A added to the corresponding previously existing data sets 426.In some cases, the probability densities associated with probabilisticmodels 424A may be unchanged after the data sets 426A are updated basedon the newly attained information. In other cases, the probabilitydensities associated with the updated probabilistic models 424A maychange. The changes may relate to parameters associated with theprobability density (for example, the variance parameter associated witha Gaussian density function), or the probability density function itselfmay change (for example, a Gaussian density function changing to a lognormal density function). Statistical processes or algorithms may beused to directly make inferences (i. e., the statistical processes oralgorithms may comprise a probabilistic model 424) or be applied toupdate probabilistic models 424A based on the new information.Statistical modeling techniques that may be applied include linear ornon-linear regression models, principal component analysis models,statistical learning models, Bayesian models, neural network models,genetic algorithm-based statistical models, and support vector machinemodels.

In FIG. 12, the statistical inferencing function 346 is depicted,according to some embodiments. Statistical inferencing function 346includes the general inferencing functions deduction 346D, induction3461, and transduction 346T. Induction 3461 and transduction 346T areboth driven by the assimilation of new data or information 452A, asreflected in the mappings 480 and 490 from the statistical inferencingmapping 346A. Induction 3461 is a generalization function that usesspecific data or information to derive a function, in this case aprobabilistic function or model 424A, to enable a general predictivemodel. In other words, the induction function 3461 preferably seeks tofind the best type of probability density function 424A to fit the dataavailable 426A. Once a probabilistic model 424A is in place, the modelcan be used by the deduction function 346D to predict specific valuesfrom the generalized model.

Transduction 346T is a more direct approach to predicting specificvalues than induction 3461 and deduction 346D. Applying a transductionapproach recognizes that, under some circumstances, there may be noreason to derive a more general solution than is necessary, i.e.deriving an entire density function from data. That is, some level ofuseful predictive capabilities may be possible without deriving anentire density function for an uncertain variable. This may beparticularly the case when the body of existing data 426A is relativelysparse. The transduction function 346T may be based on an empirical riskminimization (ERM) function applied to appropriate data sets, ortraining sets. Or, alternative functions may form the basis of thetransduction. (“The Nature of Statistical Learning Theory,” Vapnik,2000, provides a review of transduction and statistical learning.)

The deduction function 346D or the transduction function 346T may informthe design of experiment or information gathering process 344. Thus,output from the statistical inferencing function 346 may directly orindirectly feed back 328, automatically or with human assistance, to thedesign of experiment function 344, thereby enabling an adaptive designof experiment process.

In FIG. 13, an updated version 342AU of the value of information mapping342A of FIG. 9 is shown, after conducting experiments or informationgathering, and assimilating the information within the experimentaldesign and inferencing function 340. Uncertain variables 318 havecorresponding updated probabilistic models 424 a, updated data sets 426a, and updated values of information 428 a. The updated values ofinformation 428 a are derived from the value models associated with thedecision model 310, the evaluation function 320, the uncertaintymappings 341A, the updated probabilistic models 424 a, and the updateddata sets 426A.

Hence, in some embodiments, a closed loop process is enabled,integrating design of experiment 344, statistical inferencing 346, andvalue of information 342. This closed loop 716 process may be fully orpartially automated within a computer-based system.

Statistical Learning Applications

In some embodiments, statistical learning approaches may be applied bythe design of experiment function 344 to derive the next experiment oraction or set of experiments or actions to conduct. Such statisticallearning approaches may include application of support vector machinemodels or algorithms.

Support vector machine models seek to segment or classify sets of dataspanning multiple attribute dimensions. The classification of datapoints is carried out by determining a separating hyper plane (or anequivalent non-linear functional construct) that minimizes error, whilealso maximizing the distance between the closest data points of the twoseparated data set segments and the hyper plane.

FIG. 14 illustrates a three dimensional attribute space example. Asupport vector machine model of a three dimensional attribute space 800is comprised of three dimensions 810 a, 810 b, and 810 c correspondingto three different attributes of the decision model 310. For example, indecision framework 311 c, the attributes may correspond to quantities ofcomponents 314 ca. A set of data points 815 populate the attribute space800.

A separating hyper plane 820 is determined that optimally separates twosets of data points in the attribute space. The separating hyper planeoptimizes the width of the margin 821 around it as described above.

The hyper plane 820 can therefore be thought of as representing the setof points in the attribute space representing the greatest uncertaintywith regard to classification. So, for example, in a product testingapplication, points on one side of the hyper plane (plus the margin) maycorrespond to a successful product, while points on the other side maycorrespond to product failures.

However, it may be the case that predominantly higher cost components314 ca are required to achieve the properties 316 p 4 that constitutesuccess. Therefore there may be a strong incentive to increase thesharpness regarding the components 314 ca or other variables thatinfluence success and failure. Therefore, points on the separating hyperplane constitute a set of attributes that is useful to test to maximizethe expected resolution of uncertainty. In particular, a point 815 t onthe separating hyper plane 820 that represents the narrowest marginbetween the separating hyper plane 820 and the separated data sets mayconstitute a particularly good experiment or action 333 to conduct as itcan be expected to provide maximum information with regard to resolvinguncertainty associated with the boundary between the two sets of data.

It should be noted that the exact point in the attribute space selectedto conduct as an experiment 333 may be tuned on the basis of otherfactors related to the attributes comprising the attribute space 800.For example, if the cost of an attribute is not modeled as a specific,separate attribute of attribute space 800, cost considerations may beoverlaid on the candidate experiments derived by the support vectormachine model. In general, additional optimization algorithms may beapplied to take into account attributes and other variables notexplicitly incorporated in attribute space 800.

FIG. 15 illustrates a possible result of the experiment, in which thedata point 815 t 2 resulting from the experiment 815 t drives thederivation of a new separating hyper plane 820 t 2. This process ofconducting experiments and re-deriving an new separating hyper plane maycontinue indefinitely until a separating margin 821 is achieved that issmaller than a specified threshold; or by applying more global valuationfunctions, until it is determined that the net value of the nextcandidate experiment 336 is no longer positive.

In some embodiments, support vector machine models, or the same model,may be applied by either or both the design of experiment function 344and the statistical inferencing function 346. Furthermore, the margin821 of the separating hyperplane at each step of application of thesupport vector machine model may provide input 326 a or 329 to the valueof information function 342.

Information Gathering Infrastructure Decisions

As described above, in addition to adaptive decision process 300applying the value of information function 342 and/or the design ofexperiment function 344 to determining the value associated with aspecific decision associated with a decision model 310, the value ofinformation function 342 and/or the design of experiment function 344may also be semi-automatically or automatically applied to decisionsregarding the means of information gathering or experimentalinfrastructure 350 that would improve decision making in the future.Such decisions may be considered a “meta-meta-decision”.

In accordance with some embodiments, FIG. 16 is a flow chart of anadaptive experimental infrastructure process 600. The first step 602 ofthe process is the establishment of one or more decision models 310 andcorresponding evaluation criteria 320.

Corresponding to, and/or applying, the value of information function 342and/or the design of experiment function 344 of the experimental designand inferencing function 340 of FIG. 2, the expected net value ofactions 333 specifically associated with providing information toresolve uncertainties of uncertain variables of the one or more decisionmodels 310 is then determined 604. The actions 333 may be unconstrainedby the current infrastructure of the information gathering means 350.Rather, for the adaptive experimental infrastructure process 600,simulated infrastructure options may be established or generated, andthe value of information function 342 and/or the design of experimentfunction 344 may then be applied to generate net value 335 of actions333 associated with these simulated or possible infrastructures to bepotentially included in information gathering means 350. The simulatedinfrastructures may include different types of information gatheringinfrastructure and/or different capacities of specific types ofinformation gathering infrastructure.

The value of information of the actions 333 associated with the one ormore expected future direct decisions 312 and corresponding informationgathering decisions 331 may be aggregated to determine the value ofvarious simulated test infrastructure alternatives within informationgathering means 350. The value of information function 342 and/or thedesign of experiment function 344 may be integrated, and may be appliedrecursively in a “look ahead and work backwards” process to derive thevalue of various simulated test infrastructure alternatives. Dynamicprogramming models incorporating stochastics may be applied to calculatethe value of the various simulated test infrastructure alternatives.

The expected value of infrastructure options is then determined 606 bysubtracting the expected fixed costs of each potential infrastructurealternative, as well as the expected associated variable costs of eachpotential infrastructure alternative, from the expected value ofinformation gains from the expected use of the of each potentialinfrastructure alternative infrastructure within information gatheringinfrastructure 350. The simulated infrastructure alternatives associatedwith information gathering infrastructure 350 may include, but is notlimited to, for example, high throughput experimentation infrastructurefor materials science or life sciences applications, digitized knowledgebases of content, and sensing instrumentation.

The net value of the simulated infrastructure alternatives, individuallyand/or in alternative combinations, is checked 608 to determine if thecorresponding net value is positive. If the answer is “no”, then noinfrastructure alternative is recommended for implementation.

If the answer is “yes”, then the positive valued infrastructurealternatives or alternative combinations are prioritized based on themagnitude of value and/or other criteria. The infrastructure options tobe implemented are determined 610 by combining value-basedprioritizations of infrastructure alternatives and any additionaldecision criteria such as budgetary or timing constraints.

The selected infrastructure option or options may then be implemented610. Once an infrastructure option is implemented 612, it becomesincluded in the information gathering means 350 that is fed back 614 tobe used as a basis for determining the expected value of potentialinfrastructure options associated with one or more future decisions 312in step 604 of the adaptive experimental infrastructure process 600and/or as basis for determining the expected value of one or moreactions 333 associated with a decision of step 704 the adaptive decisionprocess.

In some embodiments, some or all of steps of the adaptive decisionprocess as shown in FIG. 16 are automated through computer-basedapplications. Steps 602, 604, 606, 608, 612, and 614 may all be embodiedin computer-based software, and each step may operate on a fullyautomatic basis, or on a semi-automatic basis (i.e., requiring somelevel of human intervention). The process step of implementing theinfrastructure option 612 may be fully automated when the infrastructureis embodied in computer applications, such the generation of newknowledge bases. The process step of implementing the infrastructureoption 612 may also be automated where the infrastructure comprisescomputer-based and/or robotic systems that are capable of self-assembly.

In some embodiments, the adaptive experimental infrastructure process600 may apply the methods and/or systems of Generative InvestmentProcess as disclosed in PCT Patent Application No. PCT/US2005/001348,entitled “Generative Investment Process.” In such embodiments, theinfrastructure options may comprise a combinatorial portfolio ofinvestment opportunities.

FIG. 17 illustrates an example application of the adaptive experimentalinfrastructure process 600 in the context of the uncertainty resolutioncost framework 70. Infrastructure types 72 h, 72 i, 72 j, and 72 k (darkshading) represent existing infrastructure within the informationgathering means 350. The adaptive experimental infrastructure process600 determines that additional possible infrastructure options should beimplemented, designated as 721 and 72 m (light shading). These newinfrastructure options complement the existing infrastructure byproviding differentiated degrees of cost, ability to resolveuncertainty, and time to resolve uncertainty.

Computer-Based Implementations of Adaptive Decision Process

FIG. 18 illustrates a general approach to information and computinginfrastructure support for implementation of adaptive decision processwithin a computer application-supported process. Some or all of theelements of the adaptive decision process 300 may be implemented as acomputer-supported process 300P. The elements of the adaptive decisionprocess model 300 may include activities, procedures, frameworks,models, algorithms, and sub-processes, and may map to processactivities, sub-processes, processes, computer-based systems, content,and/or workflow of computer-supported process 300P. It should beunderstood that FIG. 18 represents an exemplary process instantiation300P of the adaptive decision process 300.

In FIG. 18, the workflow of activities within an adaptive decisionprocess 700W (corresponding to a “business process” implementation ofthe adaptive decision process logic flow 700 depicted in FIG. 3) may bemanaged by a computer-based workflow application 169 that enables theappropriate sequencing of workflow. Each activity, as for example“Activity 2” 170, may be supported by on-line content or computerapplications 175. On-line content or computer applications 175 includepure content 180, a computer application 181, and a computer applicationthat includes content 182. Information or content may be accessed by theadaptive decision process workflow 700W from each of these sources,shown as content access 180 a, information access 181 a, and informationaccess 182 a. One or more computer-based applications 181,182 mayinclude some or all of the elements of the decision model 310, theevaluation function 320, the experimental design and inferencingfunction 340, the information gathering means 350 of FIG. 2. On-linecontent internal or external 180 to a computer-based application 182 mayinclude experimental results 360.

For example, content 180 may be accessed 180 a (a content access 180 a)as an activity 170 is executed. Although multiple activities aredepicted in FIG. 16, a process or sub-process may include only oneactivity. The term “content” is defined broadly herein, to includedigitally stored text, graphics, video, audio, multi-media, computerprograms or any other means of conveying relevant information. Duringexecution of the activity 170, an interactive computer application 181may be accessed. During execution of the activity 170, information 181 amay be delivered to, as well as received from the computer application181. A computer application 182, accessible by participants 200 blm inthe adaptive decision process 300P during execution of the activity 170,and providing and receiving information 182 a during execution of theactivity 170, may also contain and manage content such that content andcomputer applications and functions that support an activity 170 may becombined within a computer application 182. An unlimited number ofcontent and computer applications may support a given activity,sub-process or process. A computer application 182 may directly containthe functionality to manage workflow 169 for the adaptive decisionprocess workflow 700W, or the workflow functionality may be provided bya separate computer-based application.

In accordance with some embodiments of the present invention, FIG. 19depicts the application of adaptive recommendations to support anadaptive decision process workflow 700W. According to some embodiments,adaptive decision process may further be implemented as an adaptiveprocess or sub-process. Adaptive decision process may apply the methodsand systems disclosed in PCT Patent Application No. PCT/US2005/011951,entitled “Adaptive Recombinant Processes,” filed on Apr. 8, 2005, whichis hereby incorporated by reference as if set forth in its entirety.

In FIG. 19, the adaptive process implementation 300PA of adaptivedecision process 300 may include many of the features of the adaptivedecision process 300P in FIG. 18. Thus, the adaptive processimplementation 300PA of adaptive decision process 300 features theworkflow application 169, if applicable, with multiple activities 170,one or more of which may be linked. Further, the adaptive computer-basedapplication 925 is depicted as part of supporting content and computerapplications 175.

One or more participants 200 blm in the adaptive process implementation300PA generate behaviors associated with their participation in theprocess workflow 700W. The participation in the adaptive processimplementation 300PA may include interactions with computer-basedsystems 181 and content 180, such as content access 180 a andinformation access 181 a, but may also include behaviors not directlyassociated with interactions with computer-based systems or content.

Process participants 200 blm may be identified by the adaptivecomputer-based application 925 through any means of computer-basedidentification, including, but not limited to, sign-in protocols orbio-metric-based means of identification; or through indirect meansbased on identification inferences derived from selective process usagebehaviors 920.

The adaptive process implementation 300PA of decision process 300includes an adaptive computer-based application 925, which includes oneor more system elements or objects, each element or object beingexecutable software and/or content that is meant for direct humanaccess. The adaptive computer-based application 925 tracks and storesselective process participant behaviors 920 associated with the adaptiveprocess implementation 300PA. It should be understood that the trackingand storing of selective behaviors by the adaptive computer-basedapplication 925 may also be associated with one or more other processes,sub-processes, and activities other than a process instance of adaptiveprocess implementation 300PA. In addition to the direct tracking andstoring of selective process usage behaviors, the adaptivecomputer-based application 925 may also indirectly acquire selectivebehaviors associated with process usage through one or more othercomputer-based applications that track and store selective processparticipant behaviors.

FIG. 19 also depicts adaptive recommendations 910 being generated anddelivered by the adaptive computer-based application 925 to processparticipants 200 blm. The adaptive recommendations 910 are shown beingdelivered to one or more process participants 200 blm engaged in“Activity 2” 170. It should be understood that the adaptiverecommendations 910 may be delivered to process participants 200 blmduring any activity or any other point during participation in a processor sub-process.

The adaptive recommendations 910 delivered by the adaptivecomputer-based application 925 are informational or computing elementsor subsets of the adaptive computer-based application 925, and may takethe form of text, graphics, Web sites, audio, video, interactivecontent, other computer applications, or embody any other type or itemof information. These recommendations are generated to facilitateparticipation in, or use of, the adaptive process implementation 300PA,and associated processes, sub-processes, or activities. The adaptiverecommendations 910 may include recommended actions 333 associated withone or more decisions 312 and/or associated information gatheringdecisions 331. The recommendations may be determined, at least in part,by combining the context of what the process participant is currentlydoing and the inferred preferences or interests of the processparticipant based, at least in part, on the behaviors of one or moreprocess participants, to generate recommendations. The adaptiverecommendations 910 may also be determined, at least in part, on theintrinsic characteristics of elements, objects or items of content ofthe adaptive computer-based application 925. These intrinsiccharacteristics may include patterns of text, images, audio, or anyother information-based patterns, including statistical analysis ofexperimental information.

As the process, sub-process or activity of adaptive processimplementation 300PA is executed more often by the one or more processparticipants, the recommendations adapt to become increasinglyeffective. Hence, the adaptive process implementation 300PA of decisionprocess 300 can adapt over time to become increasingly effective.

Furthermore, the adaptive recommendations 910 may be applied toautomatically or semi-automatically self-modify 905 the structure,elements, objects, content, information, or software of a subset of theadaptive computer-based application 925, including representations ofprocess workflow. For example, the elements, objects, or items ofcontent of the adaptive computer-based application 925, or therelationships among elements, objects, or items of content associatedwith the adaptive computer-based application 925 may be modified 905based, at least in part, on inferred preferences or interests of one ormore process participants. These modifications may be based solely oninferred preferences or interests of the one or more processparticipants 200 blm derived from process usage behaviors, or themodifications may be based on inferences of preferences or interests ofprocess participants 200 blm from process usage behaviors integratedwith inferences based on the intrinsic characteristics of elements,objects or items of content of the adaptive computer-based application925. These intrinsic characteristics may include patterns of text,images, audio, or any other information-based patterns, includingstatistical analysis of experimental information.

For example, inferences based on the statistical patterns of words,phrases or numerical data within an item of content associated with theadaptive computer-based application 925 may be integrated withinferences derived from the process usage behaviors of one or moreprocess participants to generate adaptive recommendations 910 that maybe applied to deliver to participants in the process; or may be appliedto modify 905 the structure of the adaptive computer-based application925, including the elements, objects, or items of content of theadaptive computer-based application 925, or the relationships amongelements, objects, or items of content associated with the adaptivecomputer-based application 925.

Structural modifications 905 applied to the adaptive computer-basedapplication 925 enables the structure to adapt to process participantpreferences, interests, or requirements over time by embeddinginferences on these preferences, interests or requirements directlywithin the structure of the adaptive computer-based application 925 on apersistent basis.

Adaptive recommendations generated by the adaptive computer-basedapplication 925 may be applied to modify the structure, includingobjects and items of content, of other computer-based systems 175,including the computer-based workflow application 169, supporting, oraccessible by, participants in the adaptive process implementation300PA. For example, a system that manages workflow 169 may be modifiedthrough application of adaptive recommendations generated by theadaptive computer-based application 925, potentially altering activitysequencing or other workflow aspects for one or more processparticipants associated with the adaptive process implementation 300PA.In addition to adaptive recommendations 910 being delivered to processparticipants 200 blm, process participants 200 blm may also access orinteract 915 with adaptive computer-based application 925 in other ways.The access of, or interaction with, 915 the adaptive computer-basedapplication 925 by process participants 200 blm is analogous to theinteractions 182 a with computer application 182 of FIG. 18. However, adistinguishing feature of adaptive process implementation 300PA is thatthe access or interaction 915 of the adaptive computer-based application925 by process participants 200 blm may include elements of the adaptivecomputer-based application 925 that have been adaptively self-modified905 by the adaptive computer-based application 925.

System Configurations

FIG. 20 depicts various hardware topologies that the system of theadaptive decision process 300, including the process-based system 300Pand adaptive process implementation 300PA, may embody. Servers 950, 952,and 954 are shown, perhaps residing a different physical locations, andpotentially belonging to different organizations or individuals. Astandard PC workstation 956 is connected to the server in a contemporaryfashion. In this instance, the adaptive decision process 300, orfunctional subsets thereof, such as the decision model 310, may resideon the server 950, but may be accessed by the workstation 956. Aterminal or display-only device 958 and a workstation setup 960 are alsoshown. The PC workstation 956 may be connected to a portable processingdevice (not shown), such as a mobile telephony device, which may be amobile phone or a personal digital assistant (PDA). The mobile telephonydevice or PDA may, in turn, be connected to another wireless device suchas a telephone or a GPS receiver.

FIG. 20 also features a network of wireless or other portable devices962. The adaptive decision process 300 may reside, in part or as awhole, on one or more of the devices 962, periodically or continuouslycommunicating with the central server 952. A workstation 964 connectedin a peer-to-peer fashion with other computers is also shown. In thiscomputing topology, the adaptive decision process 300, as a whole or inpart, may reside on each of the peer computers 964.

Computing system 966 represents a PC or other computing system whichconnects through a gateway or other host in order to access the server952 on which the adaptive decision process 300 resides. An appliance968, includes software “hardwired” into a physical device, or mayutilize software running on another system that does not itself host thesystem upon which the adaptive decision process 300 is loaded. Theappliance 968 is able to access a computing system that hosts aninstance of the adaptive decision process 300, such as the server 952,and is able to interact with the instance of the adaptive decisionprocess 300.

The adaptive decision process 300 may include computer-based programsthat direct the operations of, or interacts with, robotic or other typesof automated instrumentation or apparatus for the purposes of attainingadditional information related to uncertain variables 318 associatedwith decision model 310. The automated instrumentation may includeinstrumentation that can be applied to materials testing, pharmaceuticaltesting, or general product formulation testing. The communication toand from such automated or semi-automated instrumentation may be throughspecial process control software. Such automated or semi-automatedinstrumentation may be used to synthesize new materials or chemicalformulations, or new pharmaceuticals. Further, the instrumentation maybe applied to conduct combinatorial chemistry techniques. Thesetechniques may include recombinant genetic techniques or the applicationof polymerase chain reaction (PCR) techniques. The adaptive decisionprocess 300 further include the information gathering instrumentation orapparatus described herein, in addition to computer-based programs thatcontrol the said instrumentation or apparatus.

Information generated by instrumentation or apparatus may be directlycommunicated to the adaptive decision process 300, enabling a real-timefeed-back loop between information acquisition and the experimentaldesign and inferencing function 340.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thescope of this present invention.

What is claimed is:
 1. A method comprising: identifying a sequence ofdecisions; identifying one or more uncertainties that influence thesequence of decisions; embodying the one or more uncertainties into oneor more computer-implemented probability distributions; applying acomputer-implemented system that maps the one or more uncertainties to asimulation of a first infrastructure that comprises a plurality ofcomputer-implemented systems, wherein the first infrastructure isapplied to support executing the sequence of decisions; simulating by acomputer-implemented system an implementation of each of a firstplurality of computer-based options, wherein each of the first pluralityof computer-based options comprises one or more actions with respect tothe first infrastructure that are expected to reduce at least one of theone or more uncertainties; updating using a computer-implemented systemat least one of the one or more computer-implemented probabilitydistributions for each of the simulated first plurality ofcomputer-based options based upon the associated expected reduction ofthe at least one of the one or more uncertainties; generating by acomputer-implemented system an expected net value of informationassociated with each of the first plurality of computer-based options,wherein the expected net value of information associated with the eachof the first plurality of computer-based options is generated byapplying the associated updated at least one of the one or moreprobability distributions; determining by application of acomputer-implemented system an option of the first plurality ofcomputer-based options that has a greatest expected net value ofinformation of the expected net values of information that areassociated with each of the first plurality of computer-based options;and updating the simulation of the first infrastructure based uponimplementing the determined computer-based option that results in asecond infrastructure.
 2. The method of claim 1, further comprising:determining a value of perfect information associated with resolving theone or more uncertainties.
 3. The method of claim 1, further comprising:applying the computer-implemented system that maps the one or moreuncertainties to the simulation of the first infrastructure, wherein thefirst infrastructure that is simulated comprises a computer-implementedsearch function.
 4. The method of claim 1, further comprising: updatingusing the computer-implemented system the at least one of the one ormore computer-implemented probability distributions for the each of thesimulated first plurality of computer-based options based upon theassociated expected reduction of the at least one of the one or moreuncertainties, wherein the updating is performed by application of aBayesian method.
 5. The method of claim 1, further comprising:generating by the computer-implemented system the expected net value ofinformation associated with the each of the first plurality ofcomputer-based options, wherein each of the net expected net values ofinformation is generated by application of a computer-based monte carlosimulation.
 6. The method of claim 1, further comprising: generating bythe computer-implemented system the expected net value of informationassociated with the each of the first plurality of computer-basedoptions, wherein each of the expected net values of information isgenerated by a look-ahead infrastructure option model.
 7. The method ofclaim 1, further comprising: simulating by the computer-implementedsystem the second infrastructure; simulating by the computer-implementedsystem an implementation of each of a second plurality of computer-basedoptions, wherein each of the second plurality of computer-based optionscomprises one or more actions with respect to the second infrastructurethat are expected to reduce the at least one of the one or moreuncertainties; determining by application of the computer-implementedsystem an option of the second plurality of computer-based options thathas a greatest expected net value of information of the expected netvalues of information that are associated with each of the secondplurality of computer-based options; and updating the simulation of thesecond infrastructure based upon implementing the determinedcomputer-based infrastructure option of the second plurality ofcomputer-based options.
 8. A system comprising one or moreprocessor-based devices configured to: access one or morecomputer-implemented probability distributions that embody one or moreuncertainties that influence a sequence of decisions; map the one ormore uncertainties to a simulated first infrastructure that comprises aplurality of computer-implemented systems, wherein the firstinfrastructure is applied to support executing the sequence ofdecisions; simulate an implementation of each of a first plurality ofcomputer-based options, wherein each of the first plurality ofcomputer-based options comprises one or more actions with respect to thefirst infrastructure that are expected to reduce at least one of the oneor more uncertainties; update at least one of the one or morecomputer-implemented probability distributions for each of the simulatedfirst plurality of computer-based options based upon the associatedexpected reduction of the at least one of the one or more uncertainties;generate an expected net value of information associated with each ofthe first plurality of computer-based options, wherein the expected netvalue of information associated with the each of the first plurality ofcomputer-based options is generated by applying the associated updatedat least one of the one or more probability distributions; determine anoption of the first plurality of computer-based options that has agreatest expected net value of information of the expected net values ofinformation that are associated with each of the first plurality ofcomputer-based options; and update the simulated first infrastructurebased upon implementing the determined computer-based option thatresults in a second infrastructure.
 9. The system of claim 8, furthercomprising the one or more processor-based devices configured to:determine a value of perfect information associated with resolving theone or more uncertainties.
 10. The system of claim 8, further comprisingthe one or more processor-based devices configured to: map the one ormore uncertainties to the simulated first infrastructure, wherein thefirst infrastructure comprises a process control system.
 11. The systemof claim 8, further comprising the one or more processor-based devicesconfigured to: update the at least one of the one or morecomputer-implemented probability distributions for the each of thesimulated first plurality of computer-based options based upon theassociated expected reduction of the at least one of the one or moreuncertainties, wherein the updating is performed by application of aBayesian method.
 12. The system of claim 8, further comprising the oneor more processor-based devices configured to: generate the expected netvalue of information associated with the each of the first plurality ofcomputer-based options, wherein each of the net expected net values ofinformation is generated by application of a computer-based monte carlosimulation.
 13. The system of claim 8, further comprising the one ormore processor-based devices configured to: generate the expected netvalue of information associated with the each of the first plurality ofcomputer-based options, wherein each of the net expected net values ofinformation is generated by a look-ahead infrastructure option model.14. The system of claim 8, further comprising the one or moreprocessor-based devices configured to: simulate the secondinfrastructure; simulate an implementation of each of a second pluralityof computer-based options, wherein each of the second plurality ofcomputer-based options comprises one or more actions with respect to thesecond infrastructure that are expected to reduce the at least one ofthe one or more uncertainties; determine an option of the secondplurality of computer-based options that has a greatest expected netvalue of information of the expected net values of information that areassociated with each of the second plurality of computer-based options;and update the updated simulated infrastructure based upon implementingthe determined computer-based infrastructure option of the secondplurality of computer-based options.
 15. An article comprising anon-transitory computer-readable medium storing instructions forenabling a processor-based system to: access one or morecomputer-implemented probability distributions that embody one or moreuncertainties that influence a sequence of decisions; map the one ormore uncertainties to a simulated first infrastructure that comprises aplurality of computer-implemented systems, wherein the firstinfrastructure is applied to support executing the sequence ofdecisions; simulate an implementation of each of a first plurality ofcomputer-based options, wherein each of the first plurality ofcomputer-based options comprises one or more actions with respect to thefirst infrastructure that are expected to reduce at least one of the oneor more uncertainties; update at least one of the one or morecomputer-implemented probability distributions for each of the simulatedfirst plurality of computer-based options based upon the associatedexpected reduction of the at least one of the one or more uncertainties;generate an expected net value of information associated with each ofthe first plurality of computer-based options, wherein the expected netvalue of information associated with the each of the first plurality ofcomputer-based options is generated by applying the associated updatedat least one of the one or more probability distributions; determine anoption of the first plurality of computer-based options that has agreatest expected net value of information of the expected net values ofinformation that are associated with each of the first plurality ofcomputer-based options; and update the simulated first infrastructurebased upon implementing the determined computer-based option thatresults in a second infrastructure.
 16. The article of claim 15, furtherstoring instructions for enabling the processor-based system to:determine a value of perfect information associated with resolving theone or more uncertainties.
 17. The article of claim 15, further storinginstructions for enabling the processor-based system to: map the one ormore uncertainties to the simulated first infrastructure, wherein thefirst infrastructure comprises a computer-implemented knowledge base.18. The article of claim 15, further storing instructions for enablingthe processor-based system to: update the at least one of the one ormore computer-implemented probability distributions for the each of thesimulated first plurality of computer-based options based upon theassociated expected reduction of the at least one of the one or moreuncertainties, wherein the updating is performed by application of aBayesian method.
 19. The article of claim 15, further storinginstructions for enabling the processor-based system to: generate theexpected net value of information associated with the each of the firstplurality of computer-based options, wherein each of the net expectednet values of information is generated by application of acomputer-based monte carlo simulation.
 20. The article of claim 15,further storing instructions for enabling the processor-based system to:simulate the second infrastructure; simulate an implementation of eachof a second plurality of computer-based options, wherein each of thesecond plurality of computer-based options comprises one or more actionswith respect to the second infrastructure that are expected to reducethe at least one of the one or more uncertainties; determine an optionof the second plurality of computer-based options that has a greatestexpected net value of information of the expected net values ofinformation that are associated with each of the second plurality ofcomputer-based options; and update the updated simulated infrastructurebased upon implementing the determined computer-based infrastructureoption of the second plurality of computer-based options.